A flywheel energy storage system (FESS) is an effective energy-saving device. It works by accelerating a rotor flywheel disc at a very high speed and maintaining the energy in the system as rotational energy. Active magnetic bearings (AMBs) are ideally suited for use at high-speed and are so used in FESSs. This work develops a mathematical model of the electromagnet force and rotor dynamic of a flywheel. The systems for controlling the position and velocity of the flywheel are designed based on the emerging approaches of an adaptive neuro-fuzzy inference system (ANFIS). Fuzzy logic has occurred as a mathematical tool to deal with the uncertainties in human perception. It also provides a framework for applying approximate human reasoning capabilities to knowledge-based systems. Additionally, ANFIS has emerged as an intelligent controller with learning and adaptive capabilities. ANFIS is combined the fuzzy logic controller (FLC) and neural networks (NNs). In the method that is developed herein, the control model uses Takagi-Sugeno fuzzy logic, in which the back-propagation algorithm processes information from neural networks to adjust suitably the parameters of the fuzzy controller, and the output control signal tracks the input signal. This method can be applied to improve the control performance of nonlinear systems. The output signal responses transient performance of systems use an ANFIS that must be trained through a learning process to yield suitable membership functions and weightings. The results of the FESS indicated that the system responds with satisfactory control performance to reduce overshoot, a zero-error steady-state, and short rise time. The proposed controller can be feasibly applied to FESS with various external disturbances, and the effectiveness of the ANFIS with self-learning and self-improving capacities is proven.
- Liu, H., and Jiang, J., “Flywheel Energy Storage- an Upswing Technology for Energy Sustainability,” Energy and Buildings, Vol. 39, pp. 599–604, 2007.
- Koshizuka, N., “R&D of Superconducting Bearing Technologies for Flywheel Energy Storage Systems,” Physica C, Vol. 445-448, pp. 1103-1108, 2006.
- Chen, S. C., Sum, N. V., Kha, L. D., and Hsu, M. M., “ANFIS Controller for an Active Magnetic Bearing System,” Fuzzy Systems (FUZZ), IEEE International Conference , pp. 1-8, 2013.
- Weiwei, Z., and Yefa, H., “A Prototype of Flywheel Energy Storage System Suspended by Active Magnetic Bearings with PID Controller,” Power and Energy Engineering Conference, pp. 1-4, 2009.
- Nicolau, V., “On PID Controller Design by Combining Pole Placement Technique with Symmetrical Optimum Criterion,” Mathematical Problems in Engineering, Vol. 2013, pp. 1-8, 2013.
- Huann, C. K., Chu, C. T., and Jhou, J. Y., “Fuzzy Control with Fuzzy Basis Function Neural Network in Magnetic Bearing System,” Industrial Electronics, Vol. 12, pp. 846-851, 2012.
- Dias, M. J., and Dourado, A., “A Self-Organizing Fuzzy Controller with a Fixed Maximum Number of Rules and an Adaptive Similarity Factor,” Fuzzy Sets and Systems, Vol. 103, pp. 27-48, 1999.
- Nurnberger, A., Nauck, D., and Kruse, R., “Neuro-Fuzzy Control Based on the Nefcon-Model: Recent Developments,” Soft Computing, Vol. 2, pp. 168-182, 1999.
- Chen, S. C., and Tung, P. C., “Application of a Rule Self-Regulating Fuzzy Controller for Robotic Deburring on Unknown Contours,” Fuzzy Sets and Systems, Vol. 110, pp. 341-350, 2000.
- Chen, S. C., Nguyen, V. S., and Chang, G., “Application of Self-Tuning Fuzzy PID Controller on Magnetic Levitation System,” The 11th Taiwan power electronics conference & exhibition, National Tsing Hua University, 2012.
- Chen, S. C., Lin, Y. J., Nguyen, V. S., and Hsu, M. M., “A Novel Fuzzy Neural Network Controller for Maglev System with Controlled-PM Electromagnets,” Spinger journal Lecture Notes in Electrical Engineering, Vol. 234, pp. 551-561, 2013.
- Schweitzer, G., Maslen, E. H., “Magnetic Bearings – Theory, Design and Application to Rotating Machinery,” Springer-Verlag, 2009.
- Mohammadpour, H. A., Mirhoseini, S. M. H., and Shoulaie, A., “Comparative Study of Proportional and TS Fuzzy Controlled GCSC for SSR Mitigation,” Power Engineering, Energy and Electrical Drives, pp. 564 - 569, 2009.
- Ajami, A., Taheri, N., and Younesi, M., “A Novel Hybrid Fuzzy/LQR Damping Oscillations Controller Using STATCOM,” Computer and Electrical Engineering, Vol. 1, pp. 348 - 352, 2009.
- Jang, J. R., “ANFIS: Adaptive- Network- Based Fuzzy Inference System,” IEEE Trans. Syst. Man Cybern, Vol. 23, pp. 665-685, 1993.
- Jang, J. S. R., and Sun, C. T., “Neuro- Fuzzy Modeling and Control,” Proc. IEEE, Vol. 83, pp. 378-406, 1995.
- Esfahanipour, A., and Parvin, M., “An ANFIS Model for Stock Price Prediction: the Case of Tehran Stock Exchange,” INISTA, pp. 44-49, 2011.
- Jamali, N., Zadeh, N., Ashraf, H., and Jamali, Z., “Robust Pareto Design of ANFIS Networks for Nonlinear Systems with Probabilistic Uncertainties,” Innovations in Intelligent Systems and Applications, pp. 300-304, 2011.
- Eltantawie, M. A., “Application of Neuro Fuzzy Reduced Order Observer in Magnetic Bearing Systems,” Multiconf. of Engineers and Computer Scientist, Vol. 2, pp. 1-6, 2010.
- Akhil, V. G., Raksha, M. B., Raut, B. M., “ANFIS Controller and Its Application,” International Journal of Engineering Research & Technology, Vol. 2, No. 2, pp. 1-5, 2013.
- Ashok, K., Kodad, S. F., and Sankar, B. V. Ram., “Modeling, Design & Simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Speed Control of Induction Motor,” International Journal of Computer Applications, Vol. 6, No. 12, pp. 29–45, 2010.
- Mote, T. P., Lokhande, S. D., “Temperature Control System Using ANFIS,” International Journal of Soft Computing and Engineering, Vol. 2, No. 1, pp. 2231-2307, 2012.
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